Fig 1. Current dominant approaches for protein engineering. (Zhang G, et al., 2020)
Our protein engineering is based on directed evolution and rational design strategies. Screen or select from designing proteins, mutagenizing proteins, and evaluating improved properties of protein variants. We can design and modify the gene encoding protein purposefully according to your needs, and obtain a genetically modified biological system that can express protein through gene recombination technology.
Fig 2. Post-translational modification of proteins using chemical biology tools. (Anne C. Conibear, 2020)
The diversity of protein functions is also produced by post-translational modifications. Through post-translational modification, the type of protein can be increased explosively, and the structure/function relationship of the protein is changed, so that the function of the protein is more perfect and the regulation is more refined. We can provide services based on chemical modification and molecular biology modification according to your requirements.
We use site saturation mutagenesis at many selected locations to design the library according to the characteristics to be improved. Through our proprietary machine learning software tools, we can effectively select in this complex and large-scale screening, reducing the need for subsequent screening.
Our platform combines directed evolution with rational computer design, and uses massively parallel sequencing to explore the sequence functional landscape of various proteins in depth. Deep sequencing and machine learning allow us to cover more sequence space, thereby increasing the possibility of identifying variants with specific and improved performance characteristics.
Creative BioMart's team of computational biologists has extensive experience in protein engineering data analysis, statistics, statistical learning methods, and molecular dynamics and simulation. We use the public domain and proprietary custom tools to build a stable and rigorous framework for rapid development and testing. We can unravel the underlying molecular mechanism of amino acids and use computer data to predict the spatial structure and function of proteins.
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